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Climate change is expected to pose significant threats to public health, particularly including vector-borne diseases. Despite dramatic recent increases in the burden of dengue that many anecdotally connect with climate change, the effect of past and future anthropogenic climate change on dengue remains poorly quantified. To assess the link between climate warming and dengue we assembled a dataset covering 21 countries in Asia and the Americas, and found a nonlinear relationship between temperature and dengue incidence with the largest impact of warming at lower temperatures (below about 20°C), peak incidence at 27.8°C, and subsequent declines at higher temperatures. Using this inferred temperature response, we estimate that historical climate change has increased dengue incidence by 18% (11 - 27%) on average across our study countries, and that future warming could further increase it by 49% (16 - 136%) to 76% (27 - 239%) by mid-century for low or high emissions scenarios, respectively, with some cooler regions projected to see dengue doubling due to warming and other currently hot regions seeing no impact or even small declines. Under the highest emissions scenario, we estimate that 262 million people are currently living in places in these 21 countries where dengue incidence is expected to more than double due to climate change by mid-century. These insights highlight the major impacts of anthropogenic warming on dengue burden across most of its endemic range, providing a foundation for public health planning and the development of strategies to mitigate future risks due to climate change., Competing Interests: Competing Interests: We declare no competing interests.

Climate warming is expected to shift the distributions of mosquitoes and mosquito-borne diseases, promoting expansions at cool range edges and contractions at warm range edges. However, whether mosquito populations could maintain their warm edges through evolutionary adaptation remains unknown. Here, we investigate the potential for thermal adaptation in Aedes sierrensis , a congener of the major disease vector species that experiences large thermal gradients in its native range, by assaying tolerance to prolonged and acute heat exposure, and its genetic basis in a diverse, field-derived population. We found pervasive evidence of heritable genetic variation in mosquito heat tolerance, and phenotypic trade-offs in tolerance to prolonged versus acute heat exposure. Further, we found genomic variation associated with prolonged heat tolerance was clustered in several regions of the genome, suggesting the presence of larger structural variants such as chromosomal inversions. A simple evolutionary model based on our data estimates that the maximum rate of evolutionary adaptation in mosquito heat tolerance will exceed the projected rate of climate warming, implying the potential for mosquitoes to track warming via genetic adaptation., Competing Interests: Competing interests statement:The authors declare no competing interest.

Temperature drives ectothermic host - parasite interactions, making them particularly sensitive to climatic variation and change. To isolate the role of temperature, lab-based studies are increasingly used to assess and forecast disease risk under current and future climate conditions. However, in the field, the effects of temperature on parasitism may be mediated by other sources of variation, including local adaptation. To address the key knowledge gaps of how temperature influences host - parasite interactions and whether thermal responses measured in controlled experiments capture infection across temperature gradients in nature, we paired an extensive field survey of parasitism-by the ciliate Lambornella clarki on its tree hole mosquito host, Aedes sierrensis -with laboratory experiments describing parasitism thermal performance curves (TPCs) for six host populations from varying climates. We also investigated the mechanisms underlying the thermal biology of the host - parasite interaction by separately measuring TPCs for infection, host immunity, and parasite growth rates. Along the west coast of North America, across an 1100 km climate gradient spanning 12°C mean rainy season temperature variation, we found that parasitism peaked at intermediately cold temperatures, and was consistent both between field seasons and with the lab experiment results. The experiments produced no evidence of host intraspecific variation in temperature sensitivity to parasitism. Importantly, parasitism peaked at temperatures below the thermal optimum for free-living L. clarki due to the balance of temperature effects on parasite growth and reproduction against the strength of the host melanization immune response. The results suggest that nonlinear responses to temperature drive parasitism in nature, and that simple lab and field studies can accurately capture the thermal biology of multilayered host - parasite interactions. Data and code for this submission are provided on Dryad: http://datadryad.org/stash/share/CfZkk4LsJzljetJJnFZMDMrjuciTXMxrkrc95I2J3tA .

Context: Land use change and deforestation drive both biodiversity loss and zoonotic disease transmission in tropical countrysides. For mosquito communities that can include disease vectors, forest loss has been linked to reduced biodiversity and increased vector presence. The spatial scales at which land use and tree cover shape mosquito communities present a knowledge gap relevant to both biodiversity and public health., Objectives: We investigated the responses of mosquito species richness and Aedes albopictus disease vector presence to land use and to tree cover surrounding survey sites at different spatial scales. We also investigated species compositional turnover across land uses and along environmental gradients., Methods: We paired a field survey of mosquito communities in agricultural, residential, and forested lands in rural southern Costa Rica with remotely sensed tree cover data. We compared mosquito richness and vector presence responses to tree cover measured across scales from 30m to 1000m, and across land uses. We analyzed compositional turnover between land uses and along environmental gradients of tree cover, temperature, elevation, and geographic distance., Results: Tree cover was both positively correlated with mosquito species richness and negatively correlated with the presence of the common invasive dengue vector Ae. albopictus at small spatial scales of 90 - 250m. Land use predicted community composition and Ae. albopictus presence., Conclusions: The results suggest that local tree cover preservation and expansion can support mosquito species richness and reduce disease vector presence. The identified spatial range at which tree cover shapes mosquito communities can inform the development of land management practices to protect both ecosystem and public health.

Predicting the effects of climate change on plant disease is critical for protecting ecosystems and food production. Here, we show how disease pressure responds to short-term weather, historical climate and weather anomalies by compiling a global database (4339 plant-disease populations) of disease prevalence in both agricultural and wild plant systems. We hypothesised that weather and climate would play a larger role in disease in wild versus agricultural plant populations, which the results supported. In wild systems, disease prevalence peaked when the temperature was 2.7°C warmer than the historical average for the same time of year. We also found evidence of a negative interactive effect between weather anomalies and climate in wild systems, consistent with the idea that climate maladaptation can be an important driver of disease outbreaks. Temperature and precipitation had relatively little explanatory power in agricultural systems, though we observed a significant positive effect of current temperature. These results indicate that disease pressure in wild plants is sensitive to nonlinear effects of weather, weather anomalies and their interaction with historical climate. In contrast, warmer temperatures drove risks for agricultural plant disease outbreaks within the temperature range examined regardless of historical climate, suggesting vulnerability to ongoing climate change., (© 2025 John Wiley & Sons Ltd.)

Despite the increasing burden of dengue in Kenya and Africa, the introduction and expansion of the virus in the region remain poorly understood. The objective of this study is to examine the genetic diversity and evolutionary histories of dengue virus (DENV) serotypes 1 and 3 in Kenya and contextualize their circulation within circulation dynamics in the broader African region. Viral RNA was extracted from samples collected from a cohort of febrile patients recruited at clinical sites in Kenya from 2013 to 2022. Samples were tested by polymerase chain reaction (PCR) for DENV presence. Five DENV-positive samples were serotyped, and complete viral genomes for phylogenetic inference were obtained via sequencing on Illumina platforms. Sequences generated in our study were combined with global datasets of sequences, and Bayesian and maximum likelihood methods were used to infer phylogenetic trees and geographic patterns of spread with a focus on Kenya and Africa as a whole. Four new DENV-1 and one new DENV-3 genomes were successfully sequenced and combined with 328 DENV-1 and 395 DENV-3 genomes from elsewhere for phylogenetic analyses. The DENV-1 sequences from our study formed a monophyletic cluster with an inferred common ancestor in 2019 (most recent common ancestor 2019 and 95% high posterior density 2018-19), which was closely related to sequences from Tanzania. The single DENV-3 sequence clustered with sequences from Tanzania and Kenya, was collected between 2017 and 2019 and was related to recent outbreaks in the region. Phylogenetic trees resolved multiple clades of DENV-1 and DENV-3 concurrently circulating in Africa, introduced in the early-to mid-2000s. Three DENV-1 and four DENV-3 clades are highlighted, introduced between 2000 and 2015. Phylogeographic models suggest frequent, independent importations of DENV lineages into Kenya and Africa from East and South-East Asia via distinct geographic pathways. DENV-1 and DENV-3 evolutionary dynamics in Africa are characterized by the cocirculation of multiple recently introduced lineages. Circulating lineages are introduced via distinct geographic pathways that may be centered around regional nexus locations. Increased surveillance is required to identify key regional locations that drive spread, and dengue interventions should focus on interrupting spread at these locations., Competing Interests: None declared., (© The Author(s) 2025. Published by Oxford University Press.)

Background: Households (HH) have been traditionally described as the main environments where people are at risk of dengue (and other arbovirus) infection. Mounting entomological evidence has suggested a larger role of environments other than HH in transmission. Recently, an agent-based model (ABM) estimated that over half of infections occur in non-household (NH) environments like workplaces, markets, and recreational sites. However, the importance of human and vector mobility and the configurations of urban spaces in mediating the effects of NH on dengue transmission remains understudied., Methods: To improve our knowledge of the relevance of NH in transmission, we expanded an ABM calibrated from field data in Kenya to examine movement of people and vectors under different spatial configurations of buildings. In this model, we assessed the number of people traveling between HH and NH and their distance. Those were studied on three different urban configurations, on which the NH are spatially distributed either randomly (scattered), centered (in a single center), or clustered (in more than one cluster)., Results: Across simulations, the number of people moving is a major influential variable where higher levels of movement between HH and NH increases the number of cases. In addition, the number of cases is higher when NH are scattered. Intriguingly, the distance that people travel from HH to NH seems to have little effect on dengue burden; however, it affects the level of spatial clustering of cases., Conclusions: These results highlight the importance of NH as a major spreader of infections between HH and NH environments supporting the relevance of NH in transmission and its interaction with human movement in driving dengue dynamics., Competing Interests: Competing interests The authors declare that they have no competing interests.

Climate warming is expected to substantially impact the global landscape of mosquito-borne disease, but these impacts will vary across disease systems and regions. Understanding which diseases, and where within their distributions, these impacts are most likely to occur is critical for preparing public health interventions. While research has centered on potential warming-driven expansions in vector transmission, less is known about the potential for vectors to experience warming-driven stress or even local extirpations. In conservation biology, species risk from climate warming is often quantified through vulnerability indices such as thermal safety margins-the difference between an organism's upper thermal limit and its habitat temperature. Here, we estimated thermal safety margins for 8 mosquito species that are the vectors of malaria, dengue, chikungunya, Zika, West Nile and other major arboviruses, across their known ranges to investigate which mosquitoes and regions are most and least vulnerable to climate warming. We find that several of the most medically important mosquito vector species, including Ae. aegypti and An. gambiae, have positive thermal safety margins across the majority of their ranges when realistic assumptions of mosquito behavioral thermoregulation are incorporated. On average, the lowest climate vulnerability, in terms of both the magnitude and duration of thermal safety, was just south of the equator and at northern temperate range edges, and the highest climate vulnerability was in the subtropics. Mosquitoes living in regions including the Middle East, the western Sahara, and southeastern Australia, which are largely comprised of desert and xeric shrubland biomes, have the highest climate vulnerability across vector species., (© 2024 John Wiley & Sons Ltd.)

Background: Plasmodium vivax ( Pv ) is the predominant malaria species in countries approaching elimination. In the context of climate change, understanding environmental drivers of transmission can guide interventions, yet evidence is limited, particularly in Latin America., Objectives: We estimated the association between temperature and precipitation and Pv malaria incidence in a malaria elimination setting in Peru., Methods: We analyzed malaria incidence data from 2021-2023 from 30 communities in Loreto, Peru with hourly weather data from the ERA5 dataset and land cover data from MapBiomas. Predictors included average weekly minimum and maximum temperature, high heat (>90th percentile mean temperature), total weekly precipitation, and heavy rain (>90th percentile total precipitation). We fit non-linear distributed lag models for continuous weather predictors and generalized additive models for binary predictors and the lookback period was 2-16 weeks. Temperature models adjusted for total precipitation; precipitation models adjusted for maximum temperature. We performed subgroup analyses by season, community type, and distance to forest edge., Results: The median vs. lowest values of weekly average minimum temperature was associated with 2.16 to 3.93-fold higher incidence 3-16 weeks later (5-week lag incidence ratio (IR) =3.93 [95% CI 2.18, 7.09]); for maximum temperature, the association was hump-shaped across lags, with protective associations at 1-2 and 15-16 week lags and 1.07-1.66-fold higher incidence at 6-13 week lags. High heat (>27.5°C) was associated with 1.23 to 1.37-fold higher incidence at 5--9 week lags (9-week lag IR = 1.25 [1.02, 1.53]). Associations between total precipitation and malaria incidence were hump-shaped across lags, with the strongest positive association at 750 mm of precipitation at a 9-week lag (IR=1.56; [1.27, 1.65]). Heavy rain (>186mm) was associated with 1.22-1.60-fold higher incidence at 2-10 week lags (9-week lag IR=1.23 [1.02, 1.49])., Discussion: Higher temperatures and precipitation were generally associated with higher malaria incidence over 1-4 months., Competing Interests: Conflicts of Interest Statement The authors declare they have no conflicts of interest related to this work to disclose.

Human mobility drives the spread of many infectious diseases, yet the health impacts of changes in mobility due to new infrastructure development are poorly understood and currently not accounted for in impact assessments. We take a novel quasi-experimental approach to identifying the link between mobility and infectious disease, leveraging historical road upgrades as a proxy for regional human mobility changes. We analyzed how highway paving altered transmission of dengue-a high-burden mosquito-borne disease-via changes in human movement in the Madre de Dios region of Peru. The paving of the Interoceanic Highway through a formerly isolated region of the Amazon in 2009 provided a unique opportunity to quantify the causal impact of road paving on disease transmission. To uncover this relationship, we compared dengue incidence data from healthcare facilities in Madre de Dios near to versus far from the newly paved highway before and after paving, while controlling for observable and unobservable confounding variables (a difference-in-differences causal inference approach). We found that the paving of the highway caused at least an additional 9,826 (95% CI: 8,562-10,684) dengue cases since paving, accounting for 45.2% (95% CI: 39.4%-49.2%) of all dengue cases recorded in the region post highway paving (2009-2022). Our findings demonstrate the impact that infrastructure can have on dengue transmission, likely via its effects on human mobility. As a result, we advocate for future road construction plans in tropical regions to account for potential increases in dengue transmission during impact assessments.

The incidence of Aedes-borne pathogens has been increasing despite vector control efforts. Control strategies typically target households, where Aedes mosquitoes breed in household containers and bite indoors. However, our study in Kenyan cities Kisumu and Ukunda (2019-2022) reveals high Aedes abundance in public spaces, prompting the question: how important are non-household (NH) environments for dengue transmission and control? Using field data and human activity patterns, we developed an agent-based model simulating transmission across household (HH) and five NH environments, which was then used to evaluate preventive (before an epidemic) and reactive (after an epidemic commences) vector control scenarios. Our findings estimate over half of infections occur in NH settings, particularly workplaces, markets, and recreational sites. Control efforts in NH areas proved more effective than HH, contradicting the current global focus. Greater reductions in dengue cases occurred with early, high-coverage interventions, especially in NH locations. Additionally, local ecological factors, such as uneven water container distribution, influence control outcomes. This study underscores the importance of vector control in both household and non-household environments in endemic settings. It highlights a specific approach to inform evidence-based decision making to target limited vector control resources for optimal control., Competing Interests: Conflict of interest declaration We declare we have no competing interests.

Anthropogenic forcing is increasing the likelihood and severity of certain extreme weather events, which may catalyze outbreaks of climate-sensitive infectious diseases. Extreme precipitation events can promote the spread of mosquito-borne illnesses by creating vector habitat, destroying infrastructure, and impeding vector control. Here, we focus on Cyclone Yaku, which caused heavy rainfall in northwestern Peru from March 7th - 20th, 2023 and was followed by the worst dengue outbreak in Peru's history. We apply generalized synthetic control methods to account for baseline climate variation and unobserved confounders when estimating the causal effect of Cyclone Yaku on dengue cases across the 56 districts with the greatest precipitation anomalies. We estimate that 67 (95% CI: 30 - 87) % of cases in cyclone-affected districts were attributable to Cyclone Yaku. The cyclone significantly increased cases for over six months, causing 38,209 (95% CI: 17,454 - 49,928) out of 57,246 cases. The largest increases in dengue incidence due to Cyclone Yaku occurred in districts with a large share of low-quality roofs and walls in residences, greater flood risk, and warmer temperatures above 24° C . Analyzing an ensemble of climate model simulations, we found that extremely intense March precipitation in northwestern Peru is 42% more likely in the current era compared to a preindustrial baseline due to climate forcing. In sum, extreme precipitation like that associated with Cyclone Yaku has become more likely with climate change, and Cyclone Yaku caused the majority of dengue cases across the cyclone-affected districts.

Mosquito vectors of pathogens (e.g., Aedes, Anopheles, and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Athni et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Despite the increasing burden of dengue, the regional emergence of the virus in Kenya has not been examined. This study investigates the genetic structure and regional spread of dengue virus-2 in Kenya. Viral RNA from acutely ill patients in Kenya was enriched and sequenced. Six new dengue-2 genomes were combined with 349 publicly available genomes and phylogenies used to infer gene flow between Kenya and other countries. Analyses indicate two dengue-2 Cosmopolitan genotype lineages circulating in Kenya, linked to recent outbreaks in coastal Kenya and Burkina Faso. Lineages circulating in Western, Southern, and Eastern Africa exhibiting similar evolutionary features are also reported. Phylogeography suggests importation of dengue-2 into Kenya from East and Southeast Asia and bidirectional geneflow. Additional lineages circulating in Africa are also imported from East and Southeast Asia. These findings underscore how intermittent importations from East and Southeast Asia drive dengue-2 circulation in Kenya and Africa more broadly., (© 2024. The Author(s).)

Climate warming is expected to shift the distributions of mosquitoes and mosquito-borne diseases, facilitating expansions at cool range edges and contractions at warm range edges. However, whether mosquito populations could maintain their warm edges through evolutionary adaptation remains unknown. Here, we investigate the potential for thermal adaptation in Aedes sierrensis , a congener of the major disease vector species that experiences large thermal gradients in its native range, by assaying tolerance to prolonged and acute heat exposure, and its genetic basis in a diverse, field-derived population. We found pervasive evidence of heritable genetic variation in acute heat tolerance, which phenotypically trades off with tolerance to prolonged heat exposure. A simple evolutionary model based on our data shows that the estimated maximum rate of evolutionary adaptation in mosquito heat tolerance typically exceeds that of projected climate warming under idealized conditions. Our findings indicate that natural mosquito populations may have the potential to track projected warming via genetic adaptation. Prior climate-based projections may thus underestimate the range of mosquito and mosquito-borne disease distributions under future climate conditions., Competing Interests: Competing Interest Statement: authors declare no competing interest.

Temperature responses of many biological traits-including population growth, survival, and development-are described by thermal performance curves (TPCs) with phenomenological models like the Briere function or mechanistic models related to chemical kinetics. Existing TPC models are either simple but inflexible in shape, or flexible yet difficult to interpret in biological terms. Here we present flexTPC: a model that is parameterized exclusively in terms of biologically interpretable quantities, including the thermal minimum, optimum, and maximum, and the maximum trait value. FlexTPC can describe unimodal temperature responses of any skewness and thermal breadth, enabling direct comparisons across populations, traits, or taxa with a single model. We apply flexTPC to various microbial and entomological datasets, compare results with the Briere model, and find that flexTPC often has better predictive performance. The interpretability of flexTPC makes it ideal for modeling how thermal responses change with ecological stressors or evolve over time.

Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis is transmitted to humans through contact with the free-living infectious stage of Schistosoma spp. parasites released from freshwater snails, the parasite's obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata, B. straminea, and B. tenagophila). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails' ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Singleton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

AbstractClimate change will alter interactions between parasites and their hosts. Warming may affect patterns of local adaptation, shifting the environment to favor the parasite or host and thus changing the prevalence of disease. We assessed local adaptation to hosts and temperature in the facultative ciliate parasite Lambornella clarki , which infects the western tree hole mosquito Aedes sierrensis . We conducted laboratory infection experiments with mosquito larvae and parasites collected from across a climate gradient, pairing sympatric or allopatric populations across three temperatures that were either matched or mismatched to the source environment. Lambornella clarki parasites were locally adapted to their hosts, with 2.6 times higher infection rates on sympatric populations compared with allopatric populations, but they were not locally adapted to temperature. Infection peaked at the intermediate temperature of 12.5°C, notably lower than the optimum temperature for free-living L. clarki growth, suggesting that the host's immune response can play a significant role in mediating the outcome of infection. Our results highlight the importance of host selective pressure on parasites, despite the impact of temperature on infection success.

Variation in heat tolerance among populations can determine whether a species is able to cope with ongoing climate change. Such variation may be especially important for ectotherms whose body temperatures, and consequently, physiological processes, are regulated by external conditions. Additionally, differences in body size are often associated with latitudinal clines, thought to be driven by climate gradients. While studies have begun to explore variation in body size and heat tolerance within species, our understanding of these patterns across large spatial scales, particularly regarding the roles of plasticity and genetic differences, remains incomplete. Here, we examine body size, as measured by wing length, and thermal tolerance, as measured by the time to immobilisation at high temperatures ("thermal knockdown"), in populations of the mosquito Aedes sierrensis collected from across a large latitudinal climate gradient spanning 1300 km (34-44° N). We find that mosquitoes collected from lower latitudes and warmer climates were more tolerant of high temperatures than those collected from higher latitudes and colder climates. Moreover, body size increased with latitude and decreased with temperature, a pattern consistent with James' rule, which appears to be a result of plasticity rather than genetic variation. Our results suggest that warmer environments produce smaller and more thermally tolerant populations., (© 2024 The Author(s). Journal of Animal Ecology © 2024 British Ecological Society.)

Schistosomiasis is a neglected tropical disease caused by Schistosoma parasites. Schistosoma are obligate parasites of freshwater Biomphalaria and Bulinus snails, thus controlling snail populations is critical to reducing transmission risk. As snails are sensitive to environmental conditions, we expect their distribution is significantly impacted by global change. Here, we used machine learning, remote sensing, and 30 years of snail occurrence records to map the historical and current distribution of forward-transmitting Biomphalaria hosts throughout Brazil. We identified key features influencing the distribution of suitable habitat and determined how Biomphalaria habitat has changed with climate and urbanization over the last three decades. Our models show that climate change has driven broad shifts in snail host range, whereas expansion of urban and peri-urban areas has driven localized increases in habitat suitability. Elucidating change in Biomphalaria distribution-while accounting for non-linearities that are difficult to detect from local case studies-can help inform schistosomiasis control strategies., (© 2024. The Author(s).)

The geographical range of schistosomiasis is affected by the ecology of schistosome parasites and their obligate host snails, including their response to temperature. Previous models predicted schistosomiasis' thermal optimum at 21.7°C, which is not compatible with the temperature in sub-Saharan Africa (SSA) regions where schistosomiasis is hyperendemic. We performed an extensive literature search for empirical data on the effect of temperature on physiological and epidemiological parameters regulating the free-living stages of S. mansoni and S. haematobium and their obligate host snails, i.e., Biomphalaria spp. and Bulinus spp., respectively. We derived nonlinear thermal responses fitted on these data to parameterize a mechanistic, process-based model of schistosomiasis. We then re-cast the basic reproduction number and the prevalence of schistosome infection as functions of temperature. We found that the thermal optima for transmission of S. mansoni and S. haematobium range between 23.1-27.3°C and 23.6-27.9°C (95% CI) respectively. We also found that the thermal optimum shifts toward higher temperatures as the human water contact rate increases with temperature. Our findings align with an extensive dataset of schistosomiasis prevalence in SSA. The refined nonlinear thermal-response model developed here suggests a more suitable current climate and a greater risk of increased transmission with future warming for more than half of the schistosomiasis suitable regions with mean annual temperature below the thermal optimum., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Aslan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Temperature is increasing globally, and vector-borne diseases are particularly responsive to such increases. While it is known that temperature influences mosquito life history traits, transmission models have not historically considered population-specific effects of temperature. We assessed the interaction between Culex pipiens population and temperature in New York State (NYS) and utilized novel empirical data to inform predictive models of West Nile virus (WNV) transmission. Genetically and regionally distinct populations from NYS were reared at various temperatures, and life history traits were monitored and used to inform trait-based models. Variation in Cx. pipiens life history traits and population-dependent thermal responses account for a predicted 2.9°C difference in peak transmission that is reflected in regional differences in WNV prevalence. We additionally identified genetic signatures that may contribute to distinct thermal responses. Together, these data demonstrate how population variation contributes to significant geographic variability in arbovirus transmission with changing climates., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

The concurrent pressures of rising global temperatures, rates and incidence of species decline, and emergence of infectious diseases represent an unprecedented planetary crisis. Intergovernmental reports have drawn focus to the escalating climate and biodiversity crises and the connections between them, but interactions among all three pressures have been largely overlooked. Non-linearities and dampening and reinforcing interactions among pressures make considering interconnections essential to anticipating planetary challenges. In this Review, we define and exemplify the causal pathways that link the three global pressures of climate change, biodiversity loss, and infectious disease. A literature assessment and case studies show that the mechanisms between certain pairs of pressures are better understood than others and that the full triad of interactions is rarely considered. Although challenges to evaluating these interactions-including a mismatch in scales, data availability, and methods-are substantial, current approaches would benefit from expanding scientific cultures to embrace interdisciplinarity and from integrating animal, human, and environmental perspectives. Considering the full suite of connections would be transformative for planetary health by identifying potential for co-benefits and mutually beneficial scenarios, and highlighting where a narrow focus on solutions to one pressure might aggravate another., Competing Interests: Declaration of interests TJD holds a Natural Sciences and Engineering Research Council of Canada Discovery Grant. JEF holds a Bing-Mooney Fellowship at Stanford University and was funded by a National Institutes of Health grant. A-LMG was funded by the Tula Foundation. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Context: Land use change drives both biodiversity loss and zoonotic disease transmission in tropical countryside landscapes. Developing solutions for protecting countryside biodiversity, public health, and livelihoods requires understanding the scales at which habitat characteristics such as land cover shape biodiversity, especially for arthropods that transmit pathogens. Evidence increasingly shows that species richness for many taxa correlates with local tree cover., Objectives: We investigated whether mosquito species richness, community composition, and presence of disease vector species responded to land use and tree cover - and if so, whether at spatial scales similar to other taxa., Methods: We paired a field survey of mosquito communities in agricultural, residential, and forested lands in rural southern Costa Rica with remotely sensed tree cover data. We compared mosquito community responses to tree cover surrounding survey sites measured across scales, and analyzed community responses to land use and environmental gradients., Results: Tree cover was positively correlated with mosquito species richness, and negatively correlated with the presence of the common invasive dengue vector Aedes albopictus , particularly at small spatial scales of 80 - 200m. Land use predicted community composition and Ae. albopictus presence. Environmental gradients of tree cover, temperature, and elevation explained 7% of species turnover among survey sites., Conclusions: The results suggest that preservation and expansion of tree cover at local scales can protect biodiversity for a wide range of taxa, including arthropods, and also confer protection against disease vector occurrence. The identified spatial range of tree cover benefits can inform land management for conservation and public health protection., Competing Interests: The authors have no competing interests to declare.

Perceived experts (i.e. medical professionals and biomedical scientists) are trusted sources of medical information who are especially effective at encouraging vaccine uptake. The role of perceived experts acting as potential antivaccine influencers has not been characterized systematically. We describe the prevalence and importance of antivaccine perceived experts by constructing a coengagement network of 7,720 accounts based on a Twitter data set containing over 4.2 million posts from April 2021. The coengagement network primarily broke into two large communities that differed in their stance toward COVID-19 vaccines, and misinformation was predominantly shared by the antivaccine community. Perceived experts had a sizable presence across the coengagement network, including within the antivaccine community where they were 9.8% of individual, English-language users. Perceived experts within the antivaccine community shared low-quality (misinformation) sources at similar rates and academic sources at higher rates compared to perceived nonexperts in that community. Perceived experts occupied important network positions as central antivaccine users and bridges between the antivaccine and provaccine communities. Using propensity score matching, we found that perceived expertise brought an influence boost, as perceived experts were significantly more likely to receive likes and retweets in both the antivaccine and provaccine communities. There was no significant difference in the magnitude of the influence boost for perceived experts between the two communities. Social media platforms, scientific communications, and biomedical organizations may focus on more systemic interventions to reduce the impact of perceived experts in spreading antivaccine misinformation., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

How mosquitoes may respond to rapid climate warming remains unknown for most species, but will have major consequences for their future distributions, with cascading impacts on human well-being, biodiversity and ecosystem function. We investigated the adaptive potential of a wide-ranging mosquito species, Aedes sierrensis , across a large climatic gradient by conducting a common garden experiment measuring the thermal limits of mosquito life-history traits. Although field-collected populations originated from vastly different thermal environments that spanned over 1200 km, we found limited variation in upper thermal tolerance between populations. In particular, the upper thermal limits of all life-history traits varied by less than 3°C across the species range and, for most traits, did not differ significantly between populations. For one life-history trait-pupal development rate-we did detect significant variation in upper thermal limits between populations, and this variation was strongly correlated with source temperatures, providing evidence of local thermal adaptation for pupal development. However, we found that maximum environmental temperatures across most of the species' range already regularly exceed the highest upper thermal limits estimated under constant temperatures. This result suggests that strategies for coping with and/or avoiding thermal extremes are likely key components of current and future mosquito thermal tolerance.

Mosquito vectors of pathogens (e.g., Aedes , Anopheles , and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically-plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.90). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming.

Malaria is a life-threatening disease caused by Plasmodium parasites transmitted by Anopheles mosquitoes. In 2021, more than 247 million cases of malaria were reported worldwide, with an estimated 619,000 deaths. While malaria incidence has decreased globally in recent decades, some public health gains have plateaued, and many endemic hotspots still face high transmission rates. Understanding local drivers of malaria transmission is crucial but challenging due to the complex interactions between climate, entomological and human variables, and land use. This study focuses on highly climatically suitable and endemic areas in Côte d'Ivoire to assess the explanatory power of coarse climatic predictors of malaria transmission at a fine scale. Using data from 40 villages participating in a randomized controlled trial of a household malaria intervention, the study examines the effects of climate variation over time on malaria transmission. Through panel regressions and statistical modeling, the study investigates which variable (temperature, precipitation, or entomological inoculation rate) and its form (linear or unimodal) best explains seasonal malaria transmission and the factors predicting spatial variation in transmission. The results highlight the importance of temperature and rainfall, with quadratic temperature and all precipitation models performing well, but the causal influence of each driver remains unclear due to their strong correlation. Further, an independent, mechanistic temperature-dependent R 0 model based on laboratory data aligns well with observed malaria incidence rates, emphasizing the significance and predictability of temperature suitability across scales. By contrast, entomological variables, such as entomological inoculation rate, were not strong predictors of human incidence in this context. Finally, the study explores the predictors of spatial variation in malaria, considering land use, intervention, and entomological variables. The findings contribute to a better understanding of malaria transmission dynamics at local scales, aiding in the development of effective control strategies in endemic regions.

How mosquitoes may respond to rapid climate warming remains unknown for most species, but will have major consequences for their future distributions, with cascading impacts on human well-being, biodiversity, and ecosystem function. We investigated the adaptive potential of a wide-ranging mosquito species, Aedes sierrensis , across a large climatic gradient by conducting a common garden experiment measuring the thermal limits of mosquito life history traits. Although field-collected populations originated from vastly different thermal environments that spanned over 1,200 km, we found remarkably limited variation in upper thermal tolerance between populations, with the upper thermal limits of fitness varying by <1°C across the species range. For one life history trait-pupal development rate-we did detect significant variation in upper thermal limits between populations, and this variation was strongly correlated with source temperatures, providing evidence of local thermal adaptation for pupal development. However, we found environmental temperatures already regularly exceed our highest estimated upper thermal limits throughout most of the species range, suggesting limited potential for mosquito thermal tolerance to evolve on pace with warming. Strategies for avoiding high temperatures such as diapause, phenological shifts, and behavioral thermoregulation are likely important for mosquito persistence., Competing Interests: Conflict of Interest The authors declare that they have no conflicts of interest.

Background: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used., Methods: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.)., Results: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures., Conclusions: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping., (© 2023. BioMed Central Ltd., part of Springer Nature.)

The role of perceived experts (i.e., medical professionals and biomedical scientists) as potential anti-vaccine influencers has not been characterized systematically. We describe the prevalence and importance of anti-vaccine perceived experts by constructing a coengagement network based on a Twitter data set containing over 4.2 million posts from April 2021. The coengagement network primarily broke into two large communities that differed in their stance toward COVID-19 vaccines, and misinformation was predominantly shared by the anti-vaccine community. Perceived experts had a sizable presence within the anti-vaccine community and shared academic sources at higher rates compared to others in that community. Perceived experts occupied important network positions as central anti-vaccine nodes and bridges between the anti- and pro-vaccine communities. Perceived experts received significantly more engagements than other individuals within the anti- and pro-vaccine communities and there was no significant difference in the influence boost for perceived experts between the two communities. Interventions designed to reduce the impact of perceived experts who spread anti-vaccine misinformation may be warranted., Competing Interests: Conflicts of interest: All authors declare that there are no competing interests.

Most vector control activities in urban areas are focused on household environments; however, information relating to infection risks in spaces other than households is poor, and the relative risk that these spaces represent has not yet been fully understood. We used data-driven simulations to investigate the importance of household and non-household environments for dengue entomological risk in two Kenyan cities where dengue circulation has been reported. Fieldwork was performed using four strategies that targeted different stages of mosquitoes: ovitraps, larval collections, Prokopack aspiration, and BG-sentinel traps. Data were analyzed separately between household and non-household environments to assess mosquito presence, the number of vectors collected, and the risk factors for vector presence. With these data, we simulated vector and human populations to estimate the parameter m and mosquito-to-human density in both household and non-household environments. Among the analyzed variables, the main difference was found in mosquito abundance, which was consistently higher in non-household environments in Kisumu but was similar in Ukunda. Risk factor analysis suggests that small, clean water-related containers serve as mosquito breeding places in households as opposed to the trash- and rainfall-related containers found in non-household structures. We found that the density of vectors ( m ) was higher in non-household than household environments in Kisumu and was also similar or slightly lower between both environments in Ukunda. These results suggest that because vectors are abundant, there is a potential risk of transmission in non-household environments; hence, vector control activities should take these spaces into account.

Predicting how increasing intensity of human-environment interactions affects pathogen transmission is essential to anticipate changing disease risks and identify appropriate mitigation strategies. Vector-borne diseases (VBDs) are highly responsive to environmental changes, but such responses are notoriously difficult to isolate because pathogen transmission depends on a suite of ecological and social responses in vectors and hosts that may differ across species. Here we use the emerging tools of cumulative pressure mapping and machine learning to better understand how the occurrence of six medically important VBDs, differing in ecology from sylvatic to urban, respond to multidimensional effects of human pressure. We find that not only is human footprint-an index of human pressure, incorporating built environments, energy and transportation infrastructure, agricultural lands and human population density-an important predictor of VBD occurrence, but there are clear thresholds governing the occurrence of different VBDs. Across a spectrum of human pressure, diseases associated with lower human pressure, including malaria, cutaneous leishmaniasis and visceral leishmaniasis, give way to diseases associated with high human pressure, such as dengue, chikungunya and Zika. These heterogeneous responses of VBDs to human pressure highlight thresholds of land-use transitions that may lead to abrupt shifts in infectious disease burdens and public health needs., Competing Interests: Competing interests The authors declare no competing interests.

The spatio-temporal distribution of leishmaniasis, a parasitic vector-borne zoonotic disease, is significantly impacted by land-use change and climate warming in the Americas. However, predicting and containing outbreaks is challenging as the zoonotic Leishmania system is highly complex: leishmaniasis (visceral, cutaneous and muco-cutaneous) in humans is caused by up to 14 different Leishmania species, and the parasite is transmitted by dozens of sandfly species and is known to infect almost twice as many wildlife species. Despite the already broad known host range, new hosts are discovered almost annually and Leishmania transmission to humans occurs in absence of a known host. As such, the full range of Leishmania hosts is undetermined, inhibiting the use of ecological interventions to limit pathogen spread and the ability to accurately predict the impact of global change on disease risk. Here, we employed a machine learning approach to generate trait profiles of known zoonotic Leishmania wildlife hosts (mammals that are naturally exposed and susceptible to infection) and used trait-profiles of known hosts to identify potentially unrecognized hosts. We found that biogeography, phylogenetic distance, and study effort best predicted Leishmania host status. Traits associated with global change, such as agricultural land-cover, urban land-cover, and climate, were among the top predictors of host status. Most notably, our analysis suggested that zoonotic Leishmania hosts are significantly undersampled, as our model predicted just as many unrecognized hosts as unknown hosts. Overall, our analysis facilitates targeted surveillance strategies and improved understanding of the impact of environmental change on local transmission cycles., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Glidden et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Climate change will alter interactions between parasites and their hosts. Warming may affect patterns of local adaptation, shifting the environment to favor the parasite or host and thus changing the prevalence of disease. We assessed local adaptation in the facultative ciliate parasite Lambornella clarki , which infects the western tree hole mosquito Aedes sierrensis . We conducted laboratory infection experiments with mosquito larvae and parasites collected from across a climate gradient, pairing sympatric or allopatric populations across three temperatures that were either matched or mismatched to the source environment. L. clarki parasites were locally adapted to their hosts, with 2.6x higher infection rates on sympatric compared to allopatric populations, but were not locally adapted to temperature. Infection peaked at the intermediate temperature of 13°C. Our results highlight the importance of host selective pressure on parasites, despite the impact of temperature on infection success.

During infectious disease outbreaks, individuals may adopt protective measures like vaccination and physical distancing in response to awareness of disease burden. Prior work showed how feedbacks between epidemic intensity and awareness-based behaviour shape disease dynamics. These models often overlook social divisions, where population subgroups may be disproportionately impacted by a disease and more responsive to the effects of disease within their group. We develop a compartmental model of disease transmission and awareness-based protective behaviour in a population split into two groups to explore the impacts of awareness separation (relatively greater in- vs. out-group awareness of epidemic severity) and mixing separation (relatively greater in- vs. out-group contact rates). Using simulations, we show that groups that are more separated in awareness have smaller differences in mortality. Fatigue (i.e. abandonment of protective measures over time) can drive additional infection waves that can even exceed the size of the initial wave, particularly if uniform awareness drives early protection in one group, leaving that group largely susceptible to future infection. Counterintuitively, vaccine or infection-acquired immunity that is more protective against transmission and mortality may indirectly lead to more infections by reducing perceived risk of infection and therefore vaccine uptake. Awareness-based protective behaviour, including awareness separation, can fundamentally alter disease dynamics. Social media summary: Depending on group division, behaviour based on perceived risk can change epidemic dynamics & produce large later waves., Competing Interests: All authors declare that there are no competing interests., (© The Author(s) 2023.)

The incidence of vector-borne diseases is rising as deforestation, climate change, and globalization bring humans in contact with arthropods that can transmit pathogens. In particular, incidence of American Cutaneous Leishmaniasis (ACL), a disease caused by parasites transmitted by sandflies, is increasing as previously intact habitats are cleared for agriculture and urban areas, potentially bringing people into contact with vectors and reservoir hosts. Previous evidence has identified dozens of sandfly species that have been infected with and/or transmit Leishmania parasites. However, there is an incomplete understanding of which sandfly species transmit the parasite, complicating efforts to limit disease spread. Here, we apply machine learning models (boosted regression trees) to leverage biological and geographical traits of known sandfly vectors to predict potential vectors. Additionally, we generate trait profiles of confirmed vectors and identify important factors in transmission. Our model performed well with an average out of sample accuracy of 86%. The models predict that synanthropic sandflies living in areas with greater canopy height, less human modification, and within an optimal range of rainfall are more likely to be Leishmania vectors. We also observed that generalist sandflies that are able to inhabit many different ecoregions are more likely to transmit the parasites. Our results suggest that Psychodopygus amazonensis and Nyssomia antunesi are unidentified potential vectors, and should be the focus of sampling and research efforts. Overall, we found that our machine learning approach provides valuable information for Leishmania surveillance and management in an otherwise complex and data sparse system., Competing Interests: The authors declare that they have no conflict of interest., (Copyright: © 2023 Vadmal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement., Methods: We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill., Results: Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill., Conclusions: Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases)., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: Billions of people living in poverty are at risk of environmentally mediated infectious diseases-that is, pathogens with environmental reservoirs that affect disease persistence and control and where environmental control of pathogens can reduce human risk. The complex ecology of these diseases creates a global health problem not easily solved with medical treatment alone., Methods: We quantified the current global disease burden caused by environmentally mediated infectious diseases and used a structural equation model to explore environmental and socioeconomic factors associated with the human burden of environmentally mediated pathogens across all countries., Findings: We found that around 80% (455 of 560) of WHO-tracked pathogen species known to infect humans are environmentally mediated, causing about 40% (129 488 of 359 341 disability-adjusted life years) of contemporary infectious disease burden (global loss of 130 million years of healthy life annually). The majority of this environmentally mediated disease burden occurs in tropical countries, and the poorest countries carry the highest burdens across all latitudes. We found weak associations between disease burden and biodiversity or agricultural land use at the global scale. In contrast, the proportion of people with rural poor livelihoods in a country was a strong proximate indicator of environmentally mediated infectious disease burden. Political stability and wealth were associated with improved sanitation, better health care, and lower proportions of rural poverty, indirectly resulting in lower burdens of environmentally mediated infections. Rarely, environmentally mediated pathogens can evolve into global pandemics (eg, HIV, COVID-19) affecting even the wealthiest communities., Interpretation: The high and uneven burden of environmentally mediated infections highlights the need for innovative social and ecological interventions to complement biomedical advances in the pursuit of global health and sustainability goals., Funding: Bill & Melinda Gates Foundation, National Institutes of Health, National Science Foundation, Alfred P. Sloan Foundation, National Institute for Mathematical and Biological Synthesis, Stanford University, and the US Defense Advanced Research Projects Agency., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Effectively controlling heartworm disease-a major parasitic disease threatening animal health in the US and globally-requires understanding the local ecology of mosquito vectors involved in transmission. However, the key vector species in a given region are often unknown and challenging to identify. Here we investigate (i) the key vector species associated with transmission of the parasite, Dirofilaria immitis, in California and (ii) the climate and land cover drivers of vector presence., Methods: To identify key mosquito vectors involved in transmission, we incorporated long-term, finely resolved mosquito surveillance data and dog heartworm case data in a statistical modeling approach (fixed-effects regression) that rigorously controls for other unobserved drivers of heartworm cases. We then used a flexible machine learning approach (gradient boosted machines) to identify the climate and land cover variables associated with the presence of each species., Results: We found significant, regionally specific, positive associations between dog heartworm cases and the abundance of four vector species: Aedes aegypti (Central California), Ae. albopictus (Southern California), Ae. sierrensis (Central California), and Culiseta incidens (Northern and Central California). The proportion of developed land cover was one of the most important ecological variables predicting the presence or absence of the putative vector species., Conclusion: Our results implicate three previously under-recognized vectors of dog heartworm transmission in California and indicate the land cover types in which each putative vector species is commonly found. Efforts to target these species could prioritize surveillance in these land cover types (e.g. near human dwellings in less urbanized settings for Ae. albopictus and Cs. incidens) but further investigation on the natural infection prevalence and host-biting rates of these species, as well as the other local vectors, is needed., (© 2022. The Author(s).)

The globalization of mosquito-borne arboviral diseases has placed more than half of the human population at risk. Understanding arbovirus ecology, including the role individual mosquito species play in virus transmission cycles, is critical for limiting disease. Canonical virus-vector groupings, such as Aedes- or Culex-associated flaviviruses, have historically been defined using virus detection in field-collected mosquitoes, mosquito feeding patterns, and vector competence, which quantifies the intrinsic ability of a mosquito to become infected with and transmit a virus during a subsequent blood feed. Herein, we quantitatively synthesize data from 68 laboratory-based vector competence studies of 111 mosquito-virus pairings of Australian mosquito species and viruses of public health concern to further substantiate existing canonical vector-virus groupings and quantify variation within these groupings. Our synthesis reinforces current canonical vector-virus groupings but reveals substantial variation within them. While Aedes species were generally the most competent vectors of canonical "Aedes-associated flaviviruses" (such as dengue, Zika, and yellow fever viruses), there are some notable exceptions; for example, Aedes notoscriptus is an incompetent vector of dengue viruses. Culex spp. were the most competent vectors of many traditionally Culex-associated flaviviruses including West Nile, Japanese encephalitis and Murray Valley encephalitis viruses, although some Aedes spp. are also moderately competent vectors of these viruses. Conversely, many different mosquito genera were associated with the transmission of the arthritogenic alphaviruses, Ross River, Barmah Forest, and chikungunya viruses. We also confirm that vector competence is impacted by multiple barriers to infection and transmission within the mesenteron and salivary glands of the mosquito. Although these barriers represent important bottlenecks, species that were susceptible to infection with a virus were often likely to transmit it. Importantly, this synthesis provides essential information on what species need to be targeted in mosquito control programs., Competing Interests: The authors have declared that no competing interests exist.

Our world is undergoing rapid planetary changes driven by human activities, often mediated by economic incentives and resource management, affecting all life on Earth. Concurrently, many infectious diseases have recently emerged or spread into new populations. Mounting evidence suggests that global change-including climate change, land-use change, urbanization, and global movement of individuals, species, and goods-may be accelerating disease emergence by reshaping ecological systems in concert with socioeconomic factors. Here, we review insights, approaches, and mechanisms by which global change drives disease emergence from a disease ecology perspective. We aim to spur more interdisciplinary collaboration with economists and identification of more effective and sustainable interventions to prevent disease emergence. While almost all infectious diseases change in response to global change, the mechanisms and directions of these effects are system specific, requiring new, integrated approaches to disease control that recognize linkages between environmental and economic sustainability and human and planetary health.

Parasitism is expected to change in a warmer future, but whether warming leads to substantial increases in parasitism remains unclear. Understanding how warming effects on parasitism in individual hosts (e.g. parasite load) translate to effects on population-level parasitism (e.g. prevalence, R 0 ) remains a major knowledge gap. We conducted a literature review and identified 24 host-parasite systems that had information on the temperature dependence of parasitism at both individual host and host population levels: 13 vector-borne systems and 11 environmentally transmitted systems. We found a strong positive correlation between the thermal optima of individual- and population-level parasitism, although several of the environmentally transmitted systems exhibited thermal optima >5°C apart between individual and population levels. Parasitism thermal optima were close to vector performance thermal optima in vector-borne systems but not hosts in environmentally transmitted systems, suggesting these thermal mismatches may be more common in certain types of host-parasite systems. We also adapted and simulated simple models for both types of transmission modes and found the same pattern across the two modes: thermal optima were more strongly correlated across scales when there were more traits linking individual- to population-level processes. Generally, our results suggest that information on the temperature dependence, and specifically the thermal optimum, at either the individual or population level should provide a useful-although not quantitatively exact-baseline for predicting temperature dependence at the other level, especially in vector-borne parasite systems. Environmentally transmitted parasitism may operate by a different set of rules, in which temperature dependence is decoupled in some systems, requiring the need for trait-based studies of temperature dependence at individual and population levels., (© 2022 The Authors. Journal of Animal Ecology © 2022 British Ecological Society.)

Rationale: Avoiding excess health damages attributable to climate change is a primary motivator for policy interventions to reduce greenhouse gas emissions. However, the health benefits of climate mitigation, as included in the policy assessment process, have been estimated without much input from health experts. Objectives: In accordance with recommendations from the National Academies in a 2017 report on approaches to update the social cost of greenhouse gases (SC-GHG), an expert panel of 26 health researchers and climate economists gathered for a virtual technical workshop in May 2021 to conduct a systematic review and meta-analysis and recommend improvements to the estimation of health impacts in economic-climate models. Methods: Regionally resolved effect estimates of unit increases in temperature on net all-cause mortality risk were generated through random-effects pooling of studies identified through a systematic review. Results: Effect estimates and associated uncertainties varied by global region, but net increases in mortality risk associated with increased average annual temperatures (ranging from 0.1% to 1.1% per 1°C) were estimated for all global regions. Key recommendations for the development and utilization of health damage modules were provided by the expert panel and included the following: not relying on individual methodologies in estimating health damages; incorporating a broader range of cause-specific mortality impacts; improving the climate parameters available in economic models; accounting for socioeconomic trajectories and adaptation factors when estimating health damages; and carefully considering how air pollution impacts should be incorporated in economic-climate models. Conclusions: This work provides an example of how subject-matter experts can work alongside climate economists in making continued improvements to SC-GHG estimates.